Various systems, such as various types of vehicles and the systems and subsystems that comprise the vehicles, may be subject to potentially severe environmental conditions, shock, vibration, and normal component wear. These conditions, as well as others, may have deleterious effects on vehicle operability. These deleterious effects, if experienced during operation, may require some type of corrective action. Hence, most notably in the context of vehicles, health monitoring/management systems are increasingly being used.
Vehicle health monitoring/management systems monitor various health/maintenance-related characteristics of the vehicle, and include a maintenance reasoner. Typically, a maintenance reasoner processes the health/maintenance-related information and provides maintenance action recommendations in the order of utility. Presently known maintenance reasoners are deterministic, meaning the reasoners provide the same result every time they are supplied with the same health/maintenance-related information.
Although deterministic maintenance reasoners are generally reliable and robust, they do suffer certain drawbacks. In particular, deterministic reasoners cannot properly function with incomplete information, and thus require certain inputs to produce an output. If some of these inputs are not available, then certain values are assumed for these inputs. Using assumed values for unavailable inputs can lead to non-optimal results.
Hence, there is a need for a maintenance reasoner that will provide optimal results when complete information is not available. The present invention addresses at least this need.